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This paper explores the role of emotion recognition in improving student learning by monitoring psychological states in real-time. We introduce a multi-face automated emotion recognition system that analyzes the emotions of multiple students simultaneously, evaluated on the Emotion PTIT dataset, which consists of students in a classroom, manually labeled with 1,500 images and 300 videos for face recognition and classroom detection tasks, respectively. To enhance classroom dynamics, we propose a new formula for evaluating engagement based on emotional data. The system, designed for low-power IoT devices, addresses challenges like load balancing and latency, processing live video feeds to assess classroom interactions. Our prototype can detect up to 50 faces at 25 FPS with an accuracy of 88.18%.

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Real-Time Multi-face Emotion Recognition in Classroom

  • 会议论文

  • 发表日期：

    2025年

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  • Uyen Vuong,
  • Hai Nguyen,
  • Hieu Dao,
  • Hung Nguyen,
  • Cong Tran

摘要

This paper explores the role of emotion recognition in improving student learning by monitoring psychological states in real-time. We introduce a multi-face automated emotion recognition system that analyzes the emotions of multiple students simultaneously, evaluated on the Emotion PTIT dataset, which consists of students in a classroom, manually labeled with 1,500 images and 300 videos for face recognition and classroom detection tasks, respectively. To enhance classroom dynamics, we propose a new formula for evaluating engagement based on emotional data. The system, designed for low-power IoT devices, addresses challenges like load balancing and latency, processing live video feeds to assess classroom interactions. Our prototype can detect up to 50 faces at 25 FPS with an accuracy of 88.18%.